• 한국추진공학회지
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• 제27권1호
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• pp.37-48
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• 2023

본 연구에서는 정상 상태 및 천이 상태에 따른 항공기용 터보팬 엔진의 성능해석 모델링을 수행하였다. 대상 엔진은 Pratt & Whitney 사의 F100-PW-229으로 선정하여 팬, 고압 압축기, 연소기, 고압터빈, 저압 터빈, Mixer, 수축-확산형 노즐 등의 구성품을 모델링하였다. 또한, 이차 유로를 통한 터빈에서의 냉각 효과를 적용하였다. Simulink를 이용하여 터보팬 엔진 성능해석 프로그램을 자체 개발함에 따라 해석의 자유도가 높으며, 엔진 제어기 설계에 활용이 용이한 구성의 성능해석 프로그램을 개발하였다. 개발된 성능해석 프로그램은 상용 프로그램인 GASTURB 해석 결과와의 비교를 통하여 검증하였다.

• 한국전산구조공학회논문집
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• 제36권4호
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• pp.259-264
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• 2023

본 논문에서는 3차원 엮임 재료의 재료 물성치들을 효율적으로 분석하고 추후 최적설계 연구에 활용하기 위해서 파라메트릭 배치 해석 워크플로우를 제시하였다. 3차원 엮임 재료를 구성하는 와이어들 사이의 간격을 설계 매개변수로 하는 파라메트릭 모델에 대해서 임의의 변수 조합을 가지는 2,500개의 수치 모델을 생성하였으며, 상용 프로그램인 매트랩과 앤시스의 여러 모듈을 사용하여 체적탄성계수, 열전도도, 유체투과율과 같은 다양한 재료 물성치들을 배치 해석을 통해서 자동으로 얻어질 수 있도록 구성하였다. 이와 같이 얻어진 대용량의 재료 물성치 데이터베이스를 활용해서 회귀 분석을 수행하였으며, 그 결과 설계 변수들과 재료 물성치 사이의 경향성과 수치 해석 결과의 정확도를 검증하였다. 또한 확보된 데이터베이스를 통해서 3차원 엮임 재료의 물성치를 예측할 수 있는 인공 신경망을 구성하고 학습시켰으며, 그 결과 임의의 설계 매개변수 값들을 가지는 엮임 재료 모델에 대해서 구조 및 유체해석 과정 없이도 높은 정확도로 재료 물성치들을 추정할 수 있음을 확인하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.249-251
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• 2022

본 연구에서는 뇌파를 이용하여 드론을 제어하기 위한 기계학습을 논의한다. 드론의 이륙과 전진, 후진, 좌측 이동 그리고 우측 이동을 제어대상으로 정의하였고 이를 제어하기 위한 뇌파의 신호를 전두엽을 대상으로 하는 Fp1·Fp2 2채널 건식 전극(NeuroNicle FX2) 뇌파 측정장비를 통하여 5.19초동안 각 제어대상과 연관된 행동의 운동 심상을 눈을 뜬 상태에서 측정(Sampling Rate 250Hz, Cutoff Frequency 6~20Hz) 하였다. 측정된 뇌파신호에 대해 매틀랩의 분류학습기를 이용해서 삼중 계층 신경망, 로지스틱 회귀커널, 비선형 3차 SVM 학습을 실시하였으며 학습결과 로지스틱 회귀 커널 학습에서 드론의 이륙과 전진, 후진, 좌측 이동 그리고 우측 이동을 위한 가장 높은 정확도를 가지고 있음을 클래스 참양성률로 확인할 수 있었다.

• Advances in nano research
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• 제14권1호
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• pp.1-15
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• 2023

Driven by the scaling down of transistor node technology, graphene became of interest to many researchers following the success of its fabrication as graphene nanoribbons (GNRs). However, during the fabrication of GNRs, it is not uncommon to have defects within the GNR structures. Scaling down node technology also changes the modelling approach from the classical Boltzmann transport equation to the quantum transport theory because the quantum confinement effects become significant at sub-10 nanometer dimensions. The aim of this study is to examine the effect of Stone-Wales defects on the electronic properties of GNRs using a tight-binding model, based on Non-Equilibrium Green's Function (NEGF) via numeric computation methods using MATLAB. Armchair and zigzag edge defects are also implemented in the GNR structures to mimic the practical fabrication process. Electronic properties of pristine and defected GNRs of various lengths and widths were computed, including their band structure and density of states (DOS). The results show that Stone-Wales defects cause fluctuation in the band structure and increase the bandgap values for both armchair GNRs (AGNRs) and zigzag GNRs (ZGNRs) at every simulated width. In addition, Stone-Wales defects reduce the numerical computation DOS for both AGNRs and ZGNRs. However, when the lengths of the structures increase with fixed widths, the effect of the Stone-Wales defects become less significant.

• 한국분무공학회지
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• 제28권1호
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.

• Earthquakes and Structures
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• 제24권3호
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• pp.217-235
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• 2023

Orthogonal pairs of rollers on concave beds (OPRCB) are a low-cost, low-tech rolling-based isolating system, whose high efficiency has been shown in a previous study. However, seismic performance of OPRCB isolators has only been studied in the two-dimensional (2D) state so far. This is while their performance in the three-dimensional (3D) state differs from that of the 2D state, mainly since the vertical accelerations due to rollers' motion in their beds, simultaneously in two orthogonal horizontal directions, are added up and resulting in bigger vertical inertia forces and higher rolling resistance. In this study, first, Lagrange equations were used to derive the governing equations of motion of the OPRCB-isolated buildings in 3D. Then, some regular shear-type OPRCB-isolated buildings were considered subjected to three-component excitations of far- and near-source earthquakes, and their responses were compared to those of their fixed-base counterparts. Finally, the effects of more realistic modeling and analysis were examined by comparing the responses of isolated buildings in 2D and 3D states. Response histories were obtained by the fourth-order Runge-Kutta-Nystrom method, considering the geometrical nonlinearity of isolators. Results reveal that utilizing the OPRCB isolators effectively reduces the acceleration response, however, depending on the system specifications and earthquake characteristics, the maximum responses of isolated buildings in the 3D state can be up to 40% higher than those in the 2D state.

• 수산해양기술연구
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• 제59권1호
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• pp.55-64
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• 2023

Gas turbine engines are widely used as prime movers of generator and propulsion system in warships. This study addresses the problem of designing a DS-based PID controller for speed control of the LM-2500 gas turbine engine used for propulsion in warships. To this end, we first derive a dynamic model of the LM-2500 using actual sea trail data. Next, the PRC (process reaction curve) method is used to approximate the first-order plus time delay (FOPTD) model, and the DS-based PID controller design technique is proposed according to approximation of the time delay term. The proposed controller conducts set-point tracking simulation using MATLAB (2016b), and evaluates and compares the performance index with the existing control methods. As a result of simulation at each operating point, the proposed controller showed the smallest in %OS, which means that the rpm does not change rapidly. In addition, IAE and IAC were also the smallest, showing the best result in error performance and controller effort.

• Geomechanics and Engineering
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• 제33권3호
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.158-158
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• 2021

도시에 불투수면적이 증가하고, 기후변화가 극심해져감에 따라 홍수 및 열섬현상과 같은 도시의 물 문제가 발생하고 있다. 이를 해결하기 위한 정책의 일환으로 우리나라의 녹색건축인증제도(Green Standard for Energy and Environmental Design, G-SEED)에서는 물순환 관리를 평가하고 있다. 하지만, 현재 G-SEED의 평가방법을 살펴보면 빗물관리시설의 설치 정도로 평가하고 있고, 강우 특성 또한 고려되고 있지 않다. 그러므로 본 연구에서는 G-SEED의 빗물관리 및 이용 항목에 대해 수문 모델을 통해 효과를 정량화함으로써 성능에 따라 평가할 수 있는 방법을 제안하였다. 빗물관리 항목에서는 유출저감률을, 빗물이용 항목에서는 빗물이용률을 평가지표로 선정하였고, 각 평가인자를 산출하기 위하여 개념모델을 적용하였다. 빗물이용시설의 경우 초기우수배제장치 용량과 필터 효율에 따른 빗물유입량의 변화와 급수인원에 따른 수요량 변화를 고려한 수문모델을 개발하였고, 수요량과 빗물저장조 용량에 따른 유출저감률과 빗물이용률을 알아보기 위해 MATLAB을 이용하여 모의해보았다. 또한, 옥상녹화의 경우에는 강우, 저류, 증발산, 유출을 고려한 수문흐름모델을 적용하였고, 토층의 두께와 배수(저장) 층의 용량에 따라 모의하여 평가기준을 선정하였다. 제안된 수문모델의 검증을 위하여 서울대학교 기숙사와 35동 옥상녹화의 실측데이터를 비교하였고, 적용성 평가를 위해 RMSE(Root Mean Square Error)와 NSE(Nash-Sutcliffe Efficiency)를 이용하였다. 본 연구에서 제안된 방법을 통해 빗물관리시설의 수문학적 성능에 따른 평가가 가능해질 것이며 설계자와 건축가들로 하여금 실질적인 효과를 내는 시설을 설치하게끔 유도할 수 있을 것이다.

• 대한조선학회논문집
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• 제60권3호
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• pp.155-164
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• 2023

About 75% of vessel collision accidents are caused by human error, which causes enormous economic loss, environmental pollution, and human casualties, thus research on automatic collision avoidance of vessels is being actively conducted. In addition, vessels must comply with the COLREGs rules stipulated by IMO when performing collision avoidance with other vessels in motion. In this study, the collision risk was calculated by estimating the position and velocity of other vessels through the Probabilistic Data Association Filter (PDAF) algorithm based on RADAR sensor data. When a collision risk is detected, we propose an event-triggered Nonlinear Model Predict Control (NMPC) algorithm that geometrically creates waypoints that satisfy COLREGs and follows them. To verify the proposed algorithm, simulations through MATLAB are performed.